Discovering key drivers of house price growth in eight Australian capital cities:1994-2017
Bibliographic record
Abstract
This study adopts the user cost of housing framework and uses dynamic models to identify the key drivers of real house price growth in Australia's eight capital cities between 1994 and 2017. The real mortgage rate and the real investment loan growth rate are found to be significantly associated with real house price growth. A 25 basis points increase in the real mortgage rate will reduce the long-run growth rate of real house price in Sydney by about 0.69 per cent per quarter. A 1 percent increase in investment loan growth per quarter will increase the long-run real house price growth by 0.95 per cent per quarter in Sydney. The results show that investor demands have a lot more influence on house price growth than compared to owner-occupied demands in most Australian capital cities.The study also discovers that the price-to-rent ratio and population growth have strong influences on real house price growth. For most Australian Capital cities, economic factors explain around 50 t0 60 per cent of the variation i the growth rate of house prices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".